Method, apparatus and system for determining if a piece of luggage contains a liquid product

ABSTRACT

A method, an apparatus and a system are provided for determining if a piece of luggage contains a liquid product comprised of a container holding a body of liquid. The piece of luggage is scanned with an X-ray scanner to generate X-ray image data conveying an image of the piece of luggage and contents thereof. The X-ray image data is processed with a computer to detect a liquid product signature in the X-ray image data and determine if a liquid product is present in the piece of luggage. A detection signal is released at an output of the computer conveying whether a liquid product was identified in the piece of luggage. The detection signal may, for example, be used in rendering a visual representation of the piece of luggage on a display device to convey information to an operator as to the presence of a liquid product in the piece of luggage.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a national stage application under 35 U.S.C. 371 ofPCT Application No. PCT/CA2010/001200 having an international filingdate of 30 Jul. 2010, which designated the United States, which PCTapplication claimed the benefit of U.S. Provisional Application No.61/230,412 filed 31 Jul. 2009, the entire disclosure of each of which isincorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to technologies for use identifying thepresence of a liquid product inside a receptacle. The invention hasnumerous applications, in particular it can be used for scanning handcarried baggage at security check points, for example but not limited toairport and train security checkpoints.

BACKGROUND OF THE INVENTION

Some liquids or combinations of liquids and other compounds may causeenough damage to bring down an aircraft. As no reliable technology-basedsolution currently exists to adequately address this threat, authoritieshave implemented a ban of most liquids, gels and aerosols in cabinbaggage.

As a result, there have been disruptions in operations (e.g., a longerscreening process; changed the focus for screeners; additionalline-ups), major inconveniences for passengers (as well as potentialhealth hazards for some) and economic concerns (e.g., increasedscreening costs; lost revenues for airlines and duty free shops; largequantities of confiscated—including hazardous—merchandise to disposeof), and so on.

Clearly, there is a need to provide a technology-based solution toaddress the threat of fluids that are flammable, explosive or commonlyused as ingredients in explosive or incendiary devices.

SUMMARY OF THE INVENTION

In accordance with a first aspect, the invention provides a method fordetermining if a piece of luggage contains a liquid product, the liquidproduct being comprised of a container holding a body of liquid. Themethod comprises receiving X-ray image data conveying an image of thepiece of luggage and contents thereof, the X-ray image data beinggenerated by scanning the luggage with an X-ray scanner. The method alsocomprises processing the X-ray image data with a computer to detect asignature of the liquid product in the X-ray image data and determine ifa liquid product is present in the piece of luggage. The method alsocomprises releasing at an output of the computer a detection signalconveying a result obtained by processing the X-ray image data.

In accordance with a specific example of implementation, the detectionsignal indicates whether a liquid product has been identified in thepiece of luggage. The detection signal may also include locationinformation conveying a location in the piece of luggage where a liquidproduct has been identified. Optionally, the detection signal conveysconfidence information indicative of a level of confidence that aportion of the X-ray image represents a liquid product.

In accordance with a specific example of implementation, processing theX-ray image data includes identifying a set of areas in the imageconveyed by the X-ray image data as candidates for containing at least aportion of a liquid product. In accordance with a specific example,processing the X-ray image data may also include processing the areas inthe identified set of areas based in part on a set of qualifying factorsin order to derive a subset of the set of areas. The set of qualifyingfactors may include one or more factors such as, for example, size ofarea, symmetry of area, Zeff of area, edge characteristics of area, graylevel of area and aspect ratio of area.

In accordance with a specific example of implementation, processing theX-ray image data includes processing the areas in the set of areasaccording to a set of rejection criterion in order to derive a subset ofthe set of areas, the set of rejection criterion allowing identifyingareas in the set of areas that are unlikely to be associated to aportion of a liquid product. The set of rejection criterion includes oneor more rejection criterion. Non-limiting examples of rejectioncriterion include, without being limited to information related to ageometry, information related to density. The set of rejection criterionmay also include information conveying signatures of articles thatregister as false positives.

In accordance with a specific example of implementation, processing theX-ray image data includes processing the areas in the set of areasaccording to a set of merging criterion to determine whether some of theareas in the set of areas can be combined to form a merged area.

In accordance with a specific example of implementation, the X-ray imagedata is obtained using a multi-view X-ray scanner and the image of thepiece of luggage and contents thereof is a first X-ray image obtained bysubjecting the piece of luggage to X-rays in a first orientation. TheX-ray image data conveys a second X-ray image of the piece of luggage,the second image being obtained by subjecting the luggage item to X-raysin a second orientation.

In accordance with another aspect, the invention provides a computerreadable storage medium storing a program element suitable for executionby a computing apparatus for determining if a piece of luggage containsa liquid product in accordance with the above described method.

In accordance with another broad aspect, the invention provides anapparatus suitable for determining if a piece of luggage contains aliquid product in accordance with the above described method.

In accordance with another aspect, the invention provides a systemsuitable for determining if a piece of luggage contains a liquidproduct, the liquid product being comprised of a container holding abody of liquid. The system comprises an inspection device for performingan X-ray inspection on the product using penetrating radiation togenerate X-ray image data associated with the product under inspection.The system also comprises an apparatus for determining if the piece ofluggage contains a liquid product in accordance with the above describedmethod. The system also comprises a display screen in communication withthe apparatus for visually conveying to an operator information relatedto a presence of a liquid product in the piece of luggage, theinformation conveyed to the operator being based on information releasedby the apparatus.

In accordance with another broad aspect, the invention provides a methodfor determining if a piece of luggage contains a liquid product, theliquid product being comprised of a container holding a body of liquid.The method comprises receiving X-ray image data conveying an image ofthe piece of luggage, the X-ray image data being generated by scanningthe luggage with an X-ray scanner. The method also comprises processingthe X-ray image data with a computer to detect a signature of the liquidproduct in the X-ray image data and determine if a liquid product ispresent in the piece of luggage. The method also comprises generating adetection signal conveying whether a liquid product has been identifiedin the piece of luggage. The method also comprises rendering a visualrepresentation of the piece of luggage on a display to conveyinformation to an operator related to a detected liquid product in thepiece of luggage, the visual representation of the piece of luggagebeing derived at least in part based on the X-ray image data and thedetection signal.

In accordance with another aspect, the invention provides a computerreadable storage medium storing a program element suitable for executionby a computing apparatus for determining if a piece of luggage containsa liquid product in accordance with the above described method.

In accordance with another broad aspect, the invention provides anapparatus suitable for determining if a piece of luggage contains aliquid product in accordance with the above described method.

In accordance with another aspect, the invention provides a systemsuitable for determining if a piece of luggage contains a liquidproduct, the liquid product being comprised of a container holding abody of liquid. The system comprises an inspection device for performingan X-ray inspection on the product using penetrating radiation togenerate X-ray image data associated with the product under inspection.The system also comprises an apparatus for determining if the piece ofluggage contains a liquid product in accordance with the above describedmethod. The system also comprises a display screen in communication withthe apparatus for visually conveying to an operator information relatedto a presence of a liquid product in the piece of luggage, theinformation conveyed to the operator being based on information releasedby the apparatus.

In accordance with a specific example of information, the visualrepresentation of the piece of luggage conveys a location in the pieceof luggage corresponding to a detected liquid product. In a non-limitedexample of implementation, the location in the piece of luggagecorresponding to the detected liquid product is conveyed by highlightinga portion of the visual representation of the piece of luggage.

Other aspects and features of the present invention will become apparentto those ordinarily skilled in the art upon review of the followingdescription of specific embodiments of the invention in conjunction withthe accompanying Figures.

BRIEF DESCRIPTION OF THE DRAWINGS

A detailed description of examples of implementation of the presentinvention is provided hereinbelow with reference to the followingdrawings, in which:

FIG. 1 is a block diagram, illustrating at a high level a system foridentifying the presence of a liquid product in a piece of luggage,according to a non-limiting example of implementation of the invention;

FIG. 2 is a block diagram of a data processing module that is acomponent of the system shown in FIG. 1;

FIG. 3 is a flowchart that illustrates the process of a liquid productdetection software executed by the data processing module shown in FIG.2;

FIG. 4 is graph of a bottom-hat filter;

FIG. 5 is a graph of a trapezoidal function for performing a fuzzy logicprocessing operation;

FIG. 6 shows an image of a liquid product when the liquid product ispartially obstructed by another object.

In the drawings, embodiments of the invention are illustrated by way ofexample. It is to be expressly understood that the description anddrawings are only for purposes of illustration and as an aid tounderstanding, and are not intended to be a definition of the limits ofthe invention.

DETAILED DESCRIPTION

FIG. 1 is a high level block diagram of an X-ray scanning system thatcan be used to determine automatically if a piece of luggage contains aliquid product. A liquid is comprised of a container, such as a bottlemade of glass, plastic, metal or any other material holding a liquid.For the purpose of the present specification “liquid” refers to a stateof matter that is neither gas nor solid and that generally takes theshape of the container in which it is put. This definition would,therefore encompass substances that are pastes or gels, in addition tosubstances having a characteristic readiness to flow. For instance,toothpaste, and other materials having the consistency of toothpaste andthat take the shape of the container in which they are put areconsidered to fall in the definition of “liquid”.

The X-ray scanning system 10 includes an X-ray scanner 12 having a X-rayscanning area 14 in which objects to be scanned are carried by aconveyor belt 16. In the specific example of implementation of theinvention, the X-ray scanning system 10 is used for scanning luggage 17,such as suitcases, bags or any other container used to store thebelongings of a traveler. Accordingly, the X-ray scanning system 10 canbe used at security checkpoints such as at airports to determine if thepiece of luggage holds liquid products.

The X-ray scanner 12 has a source of X-ray radiation 18, which typicallywould be located under the conveyor belt 16 which generates X-raystoward the object to be scanned, namely the luggage 17. The X-raysinteract with the contents of the luggage, including the liquid product.X-ray detectors (not-shown) in the drawings capture the X-rayssubsequent to the interaction and generate an X-ray image signal thatencodes the interaction.

The X-ray scanner 12 can be a single view apparatus or a multi-viewapparatus. A single view apparatus is an apparatus that generates X-rayimage data of the luggage 17 from one perspective (view). Typically, asingle view apparatus has a single radiation source which represents thepoint of view from which the X-ray image data is produced. A multi-viewapparatus provides an X-ray image data that depicts the luggage 17 fromtwo or more perspectives. In a specific example, the perspectives areorthogonal to one another, such as a bottom view and a side view. Inthis instance, several radiation sources are used, one for each view. Itis also possible to provide a third view, in order to create athree-dimensional X-ray image that shows the luggage 17 from threesides.

Note that while the present specification uses an X-ray scanner as anexample to perform the detection of the liquid product in the luggage17, other types of penetrating radiation can also be considered forperforming the scanning operation.

Irrespective of the number of views that the X-ray scanning device 12uses, it outputs X-ray image data at an output 20. The X-ray image datais digital information that encodes the interaction between the X-raysand the contents of the luggage 17. The particular data format orcommunication protocol is not critical for the success of the inventionand many different possibilities exist in this regard, without departingfrom the spirit of the invention.

A data processing apparatus 22 is connected to the output and receivesthe X-ray image data and processes the X-ray image data in order todetermine if the luggage 17 contains a liquid product. The dataprocessing apparatus 22 generates on the basis of the image processingoperation that it performed detection information that indicates if aliquid product has been identified in the luggage 17. The detectioninformation can be conveyed in a number of possible ways. In one simpleexample, the detection information can trigger an alarm that simplyindicates to a human operator that a liquid product has been identifiedin the luggage 17. The alarm can be visual or oral. In a differentpossibility, the detection information can be embedded into an imagesignal used to drive a display device at the operator console to show animage of the luggage 17. The image is generated on the basis of theX-ray image data. One or more individual images can be shown on thedisplay device. In the example illustrated in FIG. 1, the display devicehas two individual monitors 24, 26 where each individual monitor 24, 26shows a different image of the luggage 17, such as a different view ofthe luggage 17 when the X-ray scanner 12 is a multi-view apparatus. Thedetection information can be depicted on the display device inconjunction with the image of the luggage.

For instance, the detection information is such as to inform theoperator that a liquid product has been identified and also provideslocation information indicating the location of the liquid product inthe luggage 17. The location information may be conveyed by highlightingthe portion of the X-ray image that depicts the liquid product. Examplesof highlighting include drawing the outline of the portion of the X-rayimage that depicts the liquid product with a contrasting color, fillingthe entire portion of the X-ray image of interest with a pattern ofcolor, generating a mark that on the image that visually draws theattention of the observer or de-emphasizing the other areas of the X-rayimage such that only the portion of interest is clear and visible. Whena mark is being inserted in the image, the mark can be an arrow, acircle or any other geometric figure drawn adjacent the X-ray imageportion of interest.

When two or more images of the luggage 17 are rendered on the monitors24, 26 the detection information has multiple components, one for eachset of image data associated to a particular view of the luggage 17. Ina multi-view X-ray scanning system 10 context, the detection informationconveyed by the image data designates in each image the presence and thelocation of the liquid product. In this fashion, both images of theluggage 17 show to the operator the location of the liquid product inthe respective view. When the monitor 24 is set to illustrate a frontview of the luggage 17, the detection information will highlight aportion of the X-ray image showing liquid product as it would appearwhen the luggage is looked at from the front. Similarly, the monitor 26will show a side view of the luggage 17, the liquid product beinghighlighted to show how it would appear when the luggage 17 is seen fromthat perspective. This provides the operator with the ability to build amental image of the internal contents of the luggage 17 in order tobetter appreciate the location of the liquid product relative to otheritems in the luggage 17 and also to validate the results of thedetection process, namely that the highlighted areas are indeeddifferent views of a liquid product and not of something else.

The detection information can also be used as an input to a secondaryprocessing stage that is designed to automatically determine if theliquid in the liquid product is licit or illicit. The detectioninformation supplied to the secondary processing indicates how theliquid interacts with X-rays which can be used to determine if theliquid is licit or illicit. In a specific example, the detectioninformation may simply be the pixels that are within the boundary of theliquid product outline identified in the X-ray image data. Those pixelsare analyzed by the secondary processing stage to determine if theliquid product is licit or illicit. What constitutes a licit product oran illicit may differ depending on the application. In a securityscreening context, a liquid product that may explode, is corrosive orflammable would typically be considered ‘illicit’. For cargo inspectionapplications, certain categories of products may be considered illicitsuch as alcohol, for instance.

FIG. 2 is a more detailed block diagram of the data processing device22. The data processing device is essentially a computing platform thathas a Central Processing Unit (CPU) 28, a machine readable storage 30connected to the CPU 28 via a data bus 32 and an Input/Output (I/O)interface 32. The machine readable storage is encoded with software thatperforms the analysis of the image data output from the X-ray scanner12. The machine readable storage may be implemented on a variety ofdifferent devices that are capable of storing data. The I/O 32 is theinterface that receives external signals which in turn are processed bythe software and also outputs the data that conveys the results of theprocessing. In the example described, the X-ray image data is generatedat the output 20 of the X-ray scanner 12 is input at the I/O 32 at 34.The detection information generated by the data processing device 22 isoutput from the I/O 32 and it is conveyed in an image signal driving themonitors 24, 26.

FIG. 3 is a flowchart that illustrates the general operation of thesoftware as it is being executed by the CPU 28. For completeness, theflowchart also illustrates some additional steps that are not directlyimplemented by the software.

The process starts at step 38, followed by step 40 at which the X-rayscanner 12 performs a scan of the luggage 17. This step assumes that theluggage 17 has been previously transported by the conveyor belt 16 inthe scanning area 14. The X-ray scan generates X-ray image data that canbe single view X-ray image data or multi-view X-ray image data asdiscussed earlier.

At step 42, the X-ray image data is transmitted to the data processingdevice 22 via the I/O 32. The X-ray image data is then stored in themachine readable storage 30 and it is ready for processing.

At step 44, a first processing thread is initiated. The X-ray image datais de-cluttered in order to remove from the image or to reduce thepresence in the image of areas that are contrasting with neighboringareas. The specific purpose is to entirely remove or reduce the presencein image areas that potentially represent liquid products elements thatare associated with another article which overlays the liquid product inthe luggage 17. An example would be a clothes hanger that overlaps withthe liquid product. The clothes hanger is made of metal that attenuatesX-rays much more than the liquid product and would then clearly contrastwith the liquid product image. Another example is an electrical wirethat overlaps with the liquid product. The different X-ray attenuationcharacteristics of these articles will show in the image very clearly.

For image processing purposes it is generally desirable to “morph” thesmaller component with the larger background (the liquid product) inorder to obtain a more uniform image in which the smaller component willbe less apparent. Different types of algorithms can be used to performimage de-cluttering. One such algorithm is the so called “bottom-hatfilter”. FIG. 4 is a graph of a bottom-hat filter. The bottom-hat filteris a rectangle function. Its name is related to the shape of the filterwhich looks like an inverted hat. In use, the filter operates byadjusting the pixel gray scale level of each pixel according to the grayscale level of neighboring pixels. This has the effect of smoothing theimage. Note that the adjustment of the pixel gray scale level can beweighted towards the a certain Zeff range, such that areas that displayan attenuation level which is substantially different from an areacorresponding to liquid are altered more toward that Zeff range thanother areas that manifest a smaller delta. The degree to which the imageis “de-cluttered” can vary but in general care should be taken not tode-clutter to the point where relevant image information is lost. Alsonote that de-cluttering algorithms other than a bottom hat algorithm canbe used without departing from the sprit of the invention.

The de-cluttered image is processed at step 46 to perform edgedetection. The purpose is to identify in the X-ray image data areas ofthe image that might be components of a liquid product and to definetheir edges. The output of this operation is a series of image areas(“blobs”) along with a definition of their boundaries. A boundary isconsidered to be a sharp and cleanly visible change in the X-ray imagedata attenuation values. The process uses programmable settings for whatis considered to be a “sharp change”. Step 46 performs in fact a firstpass operation that uses looser settings intended to capture anythingthat might be a boundary in the image. A second, tighter pass will beperformed later, as it will be discussed below. The identification ofblobs uses two assumptions; (1) the Zeff of a liquid product follows apredetermined Zeff variation pattern, in particular it is constant orvaries at a slow rate. Therefore, locally Zeff changes little or not atall. (2) the image of the liquid product is bound by a visible edge inthe image.

A watershed segmentation algorithm can be used for this step. Thealgorithm starts on seed regions in the image and grows these regionsprogressively until a sharp continuous boundary is found. The growthprocess is performed progressively. A blob is grown to encompasscandidate pixels in the image when the processing determines that theZeff variance between the Zeff values of the candidate pixels andadjacent pixels that are already part of the blob, is less than apredetermined amount. The Zeff variance is a configurable range that istypically set in the range from 0.1 to 1.0. The growth process stopswhen a continuous boundary is encountered where the Zeff varianceexceeds the limit.

Therefore, this operation produces a preliminary blob map thatessentially segments the X-ray image into blobs and defines theirrespective boundaries.

A second processing thread is initiated with step 48 which processes theX-ray image data as received from the data processing device 22 in orderto perform a Zeff filtering on the pixels in order to discard pixelsthat are obviously not liquids. This operation is based on theassumption that the Zeff of a liquid varies in a predetermined range andanything that is outside of this range is not a liquid. Accordingly,processing step 48 examines individually the pixels of the X-ray imagedata to retain only those that fall in the predetermined (butconfigurable) range. In a specific example of implementation, the Zeffestimate for a liquid is set in the range of about 6.0 to about 15.5.Therefore, areas of the image with Zeff estimates less than 6 or morethan 15.5 are considered to represent materials other than liquids andare discarded.

The output of the processing step 48 is therefore a Zeff map whichretains only the image areas that can potentially represent liquids.

Processing step 50 receives the blob map and filters the blob map on thebasis of the Zeff map. The purpose of this operation is to discard theblobs that have a Zeff estimate which is outside the range associatedwith a liquid.

Step 52 performs a series of additional filtering operations that relyon geometric features of liquid products in order to disqualify blobsthat are not likely to represent a liquid product or a component of aliquid product. The following filtering operations are being performed:

-   -   1. Size—the size of each blob is assessed and only blobs having        sizes in the range of 1000 to 15,000 pixels are retained. Note        that this is a configurable parameter which can be changed        according to the specific application. Blobs that are smaller        than 1000 pixels are discarded while blobs that are larger than        15,000 pixels are marked for further processing. The rationale        is that blobs less than 1000 pixels are considered too small for        further processing; accordingly they are no longer useful.        However, blobs that are larger than 15,000 pixels are not        permanently discarded on the basis that an exceedingly large        blob may in fact be made up of two or more smaller blobs that        need to be separated.    -   2. Poor solidity—The solidity of a blob is parameter that        determines the clustering or distribution of the pixels in the        blob. More specifically, the solidity of the blob is the area of        the blob divided by the area of its convex hull. For example, a        perfectly circular blob has a solidity of 1.0. A blob that has        the shape of a pen, namely a slender elongated body will have        solidity of significantly less than 1. Bottle shapes typically        have a solidity which is high and this assumption is used to        discard blobs that are not likely to represent a bottle.        Examples of solidity thresholds that can be used to discriminate        the blobs include a solidity of more than 0.5, preferably more        than 0.7 and even more preferably more than 0.8.    -   3. Aspect ratio—The aspect ratio is the ratio between the length        and the width of the blob. The processing therefore determines        for each blob two maximal dimension axes that are orthogonal and        computes the aspect ratio. Here, the assumption is that bottles        have an aspect ratio within a certain range and anything that is        outside this range is not likely to be a bottle. Examples of        aspect ratio thresholds that can be used include values between        about 10 and about 1.

If the processing operation at step 46 has identified blobs that exceedthe allowable size range, namely in excess of 15,000 pixels, which isdetermined by conditional step 54, edge detection step 46 is run onemore time but with the Zeff variance threshold set at a tighter level inan attempt to break-up the larger blobs into smaller ones. Thisoperation may be run iteratively several times with increasingly tightervariance levels. The number of variations can vary. In a specificexample of implementation, they have been set to 2. If the operationbreaks up the large blobs into smaller ones then processing returns backto step 52 that will filter the newly found blobs on the basis of size,solidity and aspect ratio. At this time, if exceedingly large blobsstill remain, they are permanently discarded.

At step 56, the remaining blobs are compared to the background to see ifthey meet a minimum differentiation factor. In this example, thedifferentiation is a difference in X-ray attenuation between the bloband the background of the blob that is the area adjacent the blobboundary. This process assumes that in the luggage, the liquid productis overlapping with some other article. As such the X-ray image of theluggage conveys, in the area of the blob compound attenuationinformation, namely attenuation due to the liquid body in the liquidproduct and also attenuation due to the article that overlaps with theliquid body. However, outside of the blob, the liquid body no longercontributes to the attenuation and the attenuation is likely due only tothe article. For the blob to represent a liquid body, the difference inX-ray attenuation between the blob and its background has to meetcertain criteria. First the blob must convey more X-ray attenuation thanthe background area and not less. Secondly, the level of differentiationin X-ray attenuation should exceed a certain threshold. In a specificexample of implementation, this threshold is set to the minimal degreeof X-ray attenuation of a liquid body can manifest in practice. Forinstance, the benchmark that can be used is acetone, which is a liquidthat attenuates X-rays to a lesser extent than other liquids which maybe encountered in a security screening context. As such, thedifferentiation threshold is set to the X-ray attenuation value that abottle of acetone manifests. The thickness of the bottle, whichcorresponds to the length of a path taken by an X-ray through thebottle, may be estimated in a number of different manners. In a specificexample of implementation, the thickness of the bottle is set to aminimum reasonable thickness given the width of the blob. In anon-limiting example, the thickness of the bottle is estimated to thewidth of the blob divided by three. In this non-limiting example thebottle is assumed to have a generally elliptical cross-section and thewidth of the blob corresponds to the semi-major axis of the ellipse. Thedivision by three is simply a value selected based on an assumption thatit would be somewhat unlikely for an elliptical bottle have a ratiobetween its semi-major axis and semi-minor axis to be greater thanthree. It is however to be understand that alternative examples ofimplementation may make use of different approaches for estimating thepath length for use in the calculation of the X-ray attenuation value.

If the degree of X-ray attenuation differentiation is less than theminimal threshold the blob is disqualified.

At step 58, the remaining blobs in the X-ray image data are compared toone another in order to qualify them in terms of likelihood ofrepresenting a liquid product or a portion of a liquid product. Thepurpose in ranking the blobs is to be able to select the top one orseveral blobs that have ranked higher than the others as likely portionsof the image representing one or more liquid products. The blobs arequalified by using a fuzzy logic process on the basis of severalfactors. The fuzzy logic process is a thresholding operation using atrapezoidal function, shown in FIG. 5 is characterized by fourthresholds a, b, c, and d. All values below a or above d receive amembership value of 0; all values above b and below c receive amembership of 1, while the values in the range from a to b and from c tod receive a membership in the range from 1 to 0, depending on theirposition with respect to the thresholds. Examples of individual factorsthat can be considered are discussed below:

-   -   1. Area—The surface area of each blob is computed by counting        the number of pixels included in the convex hull of the blob.        The convex hull is used as an estimate of the bottle size which        also provides dome tolerance to occlusion and border        segmentation issues. In one example, threshold values for a, b,        c, and d for evaluating F_(area), are 0, 0, 56666, and 65535, in        terms of number of pixels.    -   2. Blob solidity—As discussed earlier the blob solidity is        determined by computing the area of blob and dividing it by the        area of its convex hull.

In one example, threshold values for a, b, c, and d for evaluatingF_(so), are 0, 0.833, 0.888, and 1.0. Actual liquid products have asolidity that is close to the maximum value. In the specific exampleprovided, unless the solidity is above 0.8333 the solidity factor forthe candidate receives a membership of 0.

-   -   3. Blob symmetry—Since a typical liquid product has a high        degree of symmetry, whether the liquid product is seen from the        side, from the top, from the bottom or from any intermediate        side, the symmetry factor is relevant for ranking blobs. The        symmetry of a blob is computed by an algorithm which operates by        drawing candidate symmetry axes through the blob and determines        which axis provides the “best fit”. For each candidate axis, the        algorithm computes the distribution of pixels in the blob, on        each axis side. The best fit axis is the one for which the most        uniform pixel distribution can be achieved. The degree of        symmetry is expressed by a symmetry factor that has a numerical        value in the range of 1.0 to 0.0 A blob that has shape which is        perfectly symmetrical about a certain axis will have a symmetry        factor of 1. In one example, threshold values for a, b, c, and d        for evaluating F_(sy), are 0, 0.944, 1.0, and 1.0. Actual liquid        products have a symmetry that is close to the maximum value.    -   4. Blob width—Since the width of typical liquid products falls        in a certain range, blobs having width dimensions outside this        range are less likely to represent liquid products than blobs        with width dimensions within the range. The width of a blob is        determined by fitting around the blob a rectangle and then        measuring the minimum size of the rectangle. In one example,        threshold values for a, b, c, and d for evaluating F_(sy), on        images taken with an X-ray scanner manufactured by Rapiscan        Systems, model Rapiscan 620 DVAT (Dual View Advanced Technology)        are 0, 32.2, 54.4, and 65535, in terms of pixels. These values        are only examples and for other machines they may vary.    -   5. Robust Zeff Average—An estimate of the Zeff across the entire        blob is computed by estimating the median value of the Zeff        values computed in connection with all the pixels in each blob.        Through experimentation it has been found that the Zeff of a        bottle is typically lower than 11.11. In one example, threshold        values for a, b, c, and d for evaluating F_(rz), are 0, 0, 11.11        and 18.22.    -   6. Inside edge magnitude average—Each blob is processed to        determine the extent of “edges” that can be seen inside the        blob. The lower the magnitude of image features suggesting        “edges” the better since ideally the liquid body should offer a        smooth image. The inside edge factor is computed by using a 3        pixel by 3 pixel edge finding kernel, known in the art. The        kernel processes the pixels of the blob and computes an average        edge magnitude. For images taken with the Rapiscan 620 DVAT        X-ray scanner, which are 16 bits per pixel images, in has been        found through experimentation that the average inside edge        magnitude is lower than 500 for blobs representing liquid        products. In one example, threshold values for a, b, c, and d        for evaluating F_(ie), are 0, 0, 500 and 9277.78.    -   7. Gray level average—Through experimentation is has been found        that the average gray level inside a blob that represents a        liquid product is typically less than a certain threshold. For        16 bits per pixel images taken with a Rapiscan 620 DVAT X-ray        scanner, the threshold is in the area of 14,000 and more        specifically less than 13677. Thus examples of the threshold        values for computing F_(g), could be 0, 0, 13667 and 65535.    -   8. Aspect ratio—the aspect ratio of the blob was used earlier in        order to disqualify blobs that are highly unlikely to represent        liquid products, however that same factor can also be used to        rank blobs in terms of likelihood. Examples of aspect ratio        thresholds that can be used include values between about 10 and        about 1.

The next step of the process is to compute for each blob a confidencescore. The confidence score is the product of fuzzy memberships, forinstanceConfidence=100.0×F _(a) ×F _(so) ×F _(sy) ×F _(w) ×F _(rz) ×F _(ie) ×F_(g)

Note that the factors listed above are examples only. They are not allrequired in all cases and other factors can also be used.

Processing step 60 ranks the blobs based on the confidence levelcomputed at the previous step and performs a selection of the blobs thatare likely liquid products. In a specific example of implementation ablob is a likely liquid product when the confidence level of the blob isabove a certain threshold, which may vary depending on the systemtolerance for false positives. A typical setting for the threshold is80%, in other words every blob that has a confidence level in excess ofthis threshold is considered to represent a liquid product or acomponent of a liquid product.

The processing now continues with step 62 that will attempt to mergecertain blobs which in reality may be different portions of the sameliquid product. A liquid product may show in the X-ray image data as twoblobs when an object “obscures” partially the liquid product, hence itappears in the X-ray image data as two blobs separated from one another.This is shown at FIG. 6. The simplified X-ray image shows a first blob164, a second blob 166 and an object 168 that separates the blobs 164and 166. This image is produced when an object in the luggage 17overlaps with the liquid product. For instance, the object 168 may bemade of metallic material, hence it attenuates X-ray more than theliquid product. During the processing operation, the blobs 164 and 166are identified as separate blobs because of the interference by theobject 168, however in reality they are part of the same liquid product.

The merge operation attempts to find blobs that have similarcharacteristics and that logically would be parts of the same liquidproduct. Examples of characteristics that can be used in order to mergeblobs include:

-   -   1. Distance—If blobs are parts of the same bottle they cannot be        too far apart. Accordingly, the software will measure the        distance between all blob pairs in order to find possible        candidates. If the blobs in a certain pair are separated by a        distance that exceeds a threshold the software labels        disqualifies this pair for merging. In a specific example,        threshold is set to the sum of the square root of the area of        the first blob and the square root of the area of the second        blob. The candidate pairs that remain are further processed as        per the following factors.    -   2. Zeff similarity—the blobs in each candidate pair are        evaluated in terms of Zeff similarity. If the Zeff is very        similar in both blobs, the likelihood of the blobs being part of        the same liquid product increases. In a non-limiting example,        the amount of similarity is determined by generating Zeff values        distribution curves (bell curve) for each blob and by comparing        these to see the extent to which they overlap.    -   3. Symmetry—if two blobs have similar symmetry axes this is a        further indication that they are part of the same liquid        product. Consider the example of FIG. 6, in which the symmetry        axes 170, 172 for each blob 164, 166 have been computed and        drawn. A factor in assessing the similitude in the symmetry is        the angle 174 defined between the axes; the lower the angle the        higher the similitude.    -   4. Resulting solidity—the solidity factor of the combined blobs        is re-computed to determine if it tends toward typical values        associated with a liquid product or tends away from it. If it        tends toward it, this is yet a further indication that the blobs        are part of the same liquid product.    -   5. Gray level intensity—the gray level intensity of each blob is        compared to determine if they are similar. Again, a threshold        can be used here—if the gray level intensity similitude is above        a certain level the blobs are likely to be part of the same        liquid product.    -   6. Size—the total area of the merged blocks cannot exceed the        maximum allowable size for liquid products. In other words, the        merged blobs should not collective define an area that is too        large to be a liquid product. An example of maximal total area        is 15,000 pixels when the Rapiscan 620 DVAT X-ray scanner is        being used.

In making a decision on whether the blobs in the pair are part of thesame liquid product, a fuzzy logic function, similar to the onedescribed earlier can be used which takes into account the differentfactors above and computes a confidence level. Above a certainpercentage, say 80% the system assumes that the blobs are in fact partsof the same liquid product and logically associates them.

Note that the blob merging operation can be run multiple times in aniterative fashion. If the first iteration successfully merges a pair ofblobs, the operation is run again to see if another pair can be merged.In this fashion, images of liquid products that are highly fragmentedinto multiple blobs can be merged one pair of blobs at the time.

If the blob merging operation at step 62 has merged two or more blobs,then processing step 64 is initiated which re-computes the confidencevalue. Since the blobs have changed then the confidence factor that wasattributed before the merge to the respective blobs may no longer bevalid. A new confidence computation is then performed by following thesteps discussed earlier.

At step 66 the blobs that are considered to be likely liquid productswill be processed against signatures of known positives in order tofurther reduce the likelihood of incorrect results. The false positivesare objects that are typically found in a luggage and share some of thefeatures of a liquid product. The false positives filtering may be doneon a number of factors, such as geometry and density among others. Adatabase 68, which in practice may be part of the machine readablestorage 30, contains signatures of articles that may register as falsepositives. The signatures may be related to the geometry of the articleor may be related to the material that it is made from or possibly both.The step 66 will receive data from the database 68 which conveys thesignature information and processes the X-ray image data at the areaswhere blobs have been identified as likely liquid products. Theprocessing searches for the signatures and if any such signature isidentified in the X-ray image, then the blob is disqualified as a falsepositive. Examples of articles that may register as liquid productsinclude:

-   -   1. CD's. CD's have characteristic geometric shapes and Zeff that        are analogous to the shape and Zeff of a liquid product in the        X-ray image. Accordingly, the previous processing steps may not        succeed in filtering out these objects. In order to remove them        the software searches the blobs for geometric features that are        characteristic of a CD but not likely to appear in the case of a        liquid product. One such geometric feature is a circular        aperture in the center of the CD. Since the circular aperture is        of a standard dimension, the software processes the edges of the        blobs using a known image processing algorithm, to determine if        anyone of the edges or edge portions manifests a similar        geometry. Another possibility in connection with CD's, using the        material signature approach is to consider the Zeff variation        pattern from the center to the periphery. In the case of a CD,        the Zeff is likely to be constant while in the case of a liquid        product, which when seen from the bottom looks round and is        similar to a CD, will manifest a Zeff profile that varies from        the center to the periphery. Anyone of those factors, either        alone or in combination can be used to disqualify blobs in the        image.    -   2. Shoes. A shoe may appear in the X-ray image as a liquid        product because it has a shape that resembles a liquid product,        especially when the sole of the shoe is apparent in the image. A        shoe sole has a geometric form that is similar to the shape of a        liquid product namely in terms of symmetry. To filter out shoes,        a technique that works best relies on material properties namely        density. The density of a shoe is significantly less than the        density of a liquid. Accordingly, the processing will derive        from the X-ray image data an estimate of the density of the        material in the blob area of the X-ray image and will compare it        to a threshold to determine if it is too low to be a liquid.    -   3. Books and binders. Books and binders can be identified by the        presence a very even Zeff distribution over the entire area of        the blob. Accordingly, the processing will derive from the X-ray        image data the Zeff in the blob area and will determine the        amount of variation throughout that area. If the amount of        variation is below a certain minim threshold of variation, the        blob is considered as potentially corresponding to a book or a        binder and unlikely to be a liquid product. Books and binders        can also be identified by the presence of blob have a generally        large square surface. Accordingly, the processing will determine        if the shape of the blob is generally square and whether the        area of the blob exceeds a certain maximum area. If the shape of        the blob is generally square and if the size of the area of the        blob exceeds a certain threshold size, the blob is considered as        potentially corresponding to a book or a binder and unlikely to        be a liquid product.

Accordingly, the output of step 66 is a further refined list of blobsfrom which known positives have been removed.

Processing step 70 is invoked when the X-ray scanner 12 has a multi viewscanning capability. In such case, the X-ray scanner 12 generatesmultiple X-ray images of the luggage, where each image has the potentialto show a liquid product that may be present in the luggage.Accordingly, additional confidence can be gained in the results of theprocessing operation to identify liquid products in the luggage, whenthe results are consistent across the entire image set. In other words,if a liquid product is identified in one of the images and a liquidproduct is also identified in one of the images then the likelihood ofthe identification being correct increases.

When multiple views of the luggage are present, the X-ray data from eachview is processed independently as described earlier. The X-ray imagedata of each view is subjected to steps 42 to 66 to produce a list,associated with that particular view which indicates if a liquid producthas been detected in the image and also providing characterizinginformation in connection with the liquid product, such as its locationin the image along with the results of the processing performed to reachthe conclusion.

The results for each view are then correlated to determine if theymatch. The correlation operation may involve the following steps thatcan be implemented individually or in combination:

-   -   1. In the context of a dual view X-ray scanner 12 which produces        two images of the luggage 17 from views that intersect with one        another (although not necessarily positioned at 90° from one        another), the process determines if there is a geometric        correlation between the position of a blob determined to be a        liquid product in the first image and the position of a blob        also determined to be a liquid product in the second image. The        correlation is relatively simple to do since many of the        multi-view commercially available X-ray scanners 12 output with        the X-ray image data image alignment information that allows        correlating areas of one image with areas of another image. For        instance, and as it known to persons skilled in the art, an        X-ray image produced by the X-ray scanner 12 is made up by a        succession of columns, where each column is output by the linear        detector array. In the case of a dual-view X-ray scanner (or        scanners have more than two views), the columns between the        X-ray images match, such that it is possible to determine for        each column in one image the corresponding column in the other        image. As such, if a liquid product is identified on one of the        images, and its location is determined to span a certain number        of image columns, then a correlation would be deemed to exist if        a liquid product is also found in the other image over the same        image column set. Different strategies can be used to alter the        confidence level depending on whether a correlation between the        images exists. One such strategy is to increase the level of        confidence for each liquid product identified in one image for        which a correlation exists in the other image. However, if no        correlation is found for a particular liquid product one        possibility is to reduce the confidence level. Another        possibility is to leave the confidence level unchanged to        account for the possibility that in a particular view the liquid        product is obscured and not detectable.    -   2. In addition to performing inter-image correlation it is        possible to use the position information derived from the two or        more images in order to perform additional verifications to        disqualify outcomes that are physically impossible or simply        inconsistent with the geometry of the X-ray scanner 12 or the        luggage 17. In the case of a dual-view X-ray scanner, a positive        correlation of a liquid product between the two X-ray images can        be used to define a space in which the liquid product resides.        This space is essentially a three dimensional space (since two        views are available) which can be used to determine if the        liquid can possibly exist at that location. For instance if this        three dimensional space is located outside the boundaries of the        luggage, the results are disqualified since the liquid product        cannot possibly reside outside the luggage.    -   3. The correlation operation may also involve comparing Zeff        values obtained for blobs in different views to determine        whether blobs from different views are sufficiently similar to        another to be considered a potential match.

At step 72, the detection signal is issued which indicates if theluggage 17 contains a liquid product and the location of the liquidproduct in the luggage 17. As discussed previously, the detection signalcan be embedded in an image signal that shows to the operator the X-rayimage of the suitcase, emphasizing the area of the image where theliquid product is located. Another possibility is to use the detectionsignal to initiate another processing operation which determines on thebasis of the information conveyed by the X-ray image data if the liquidproduct is licit or illicit. In other words, in addition to determiningif a liquid product is present in the luggage, the system additionallywill determine the status of the liquid in terms of threat or legality.In this particular case, the detection signal will implicitly beconveyed by the results of the status assessment. For example, if theoperator is notified that a dangerous liquid is present in the luggage,then it follows that a positive detection of a liquid product in theluggage has occurred.

Although various embodiments have been illustrated, this was for thepurpose of describing, but not limiting, the invention. Variousmodifications will become apparent to those skilled in the art and arewithin the scope of this invention, which is defined more particularlyby the attached claims.

The invention claimed is:
 1. A method for performing security screeningon luggage, the method comprising: a) receiving X-ray image dataconveying an image of a piece of luggage and contents thereof, the X-rayimage data being generated by scanning the piece of luggage with anX-ray scanner; b) processing the X-ray image data using program codeexecuted by a computer to detect if a liquid product is present in thepiece of luggage, the liquid product being comprised of a containerholding a body of liquid, wherein processing the X-ray image dataincludes using the program code executed by the computer to detect aliquid product signature in the X-ray image data; c) releasing at anoutput of the computer detection results obtained by processing theX-ray image data.
 2. A method as defined in claim 1, wherein processingthe X-ray image data includes identifying in the image conveyed by theX-ray image data an area of interest.
 3. A method as defined in claim 2,comprising: a) processing the X-ray image data to estimate a thicknessof the identified area of interest; b) selectively disqualifying theidentified area of interest as being unlikely to be associated with theliquid product at least in part by processing the estimated thickness ofthe identified area of interest in combination with attenuationinformation derived from the X-ray image data.
 4. A method as defined inclaim 2, comprising: i. processing the X-ray image data to derivegeometric information associated with the identified area of interest;ii. selectively disqualifying the identified area of interest for beingunlikely to be associated with the liquid product at least in part basedon the derived geometric information.
 5. A method as defined in claim 4,comprising selectively disqualifying the identified area of interest forbeing unlikely to be associated with the liquid product at least in partbased on: a) the derived geometric information; and b) a thresholdaspect ratio.
 6. A method as defined in claim 4, comprising selectivelydisqualifying the identified area of interest for being unlikely to beassociated with the liquid product at least in part based on: a) thederived geometric information; and b) a reference solidity threshold. 7.A method as defined in claim 2, comprising selectively disqualifying theidentified area of interest for being unlikely to be associated with theliquid product at least in part based on symmetrical properties of theidentified area of interest.
 8. A method as defined in claim 2,comprising: i. processing the identified area of interest to derivedensity information associated with the identified area of interest; ii.selectively disqualifying the identified area of interest for beingunlikely to be associated with the liquid product at least in part basedon the derived density information.
 9. A method as defined in claim 2,comprising: (1) processing the identified area of interest to determinewhether it is likely to be associated with at least one object in a setof imposter objects; (2) selectively disqualifying the identified areaof interest for being unlikely to be associated with the liquid productat least in part based on results obtained in (1).
 10. A method asdefined in claim 9, wherein the set of imposter objects includes atleast one object selected from the set consisting of CDs, Shoes, booksand binders.
 11. A method as defined in claim 2, wherein identifying thearea of interest includes: a) deriving compensated X-ray image data atleast in part by compensating the X-ray image data for an amount ofobject induced X-ray attenuation resulting from X-rays travellingthrough an object in the piece of luggage, the object being unlikely tobe part of the liquid product; b) using the compensated X-ray image datawhen identifying the area of interest in the image.
 12. A method asdefined in claim 2, wherein identifying the area of interest includesidentifying in the image conveyed by the X-ray image data a set of areasof interest.
 13. A method as defined in claim 12, comprising processingat least some of the identified areas of interest according to a set ofmerging criterion to determine whether two or more areas of interest canbe combined to form a merged area of interest.
 14. A method as definedin claim 1 wherein the detection results convey a location in the pieceof luggage corresponding to the liquid product.
 15. A method as definedin claim 1, said method comprising rendering a visual representation ofthe piece of luggage, the visual representation of the piece of luggagebeing derived at least in part based on the X-ray image data and thedetection results and conveying to an operator a location in the pieceof luggage corresponding to the liquid product.
 16. A method as definedin claim 15, wherein the location in the piece of luggage correspondingto the liquid product is conveyed by highlighting a portion of thevisual representation of the piece of luggage.
 17. A method as definedin claim 1, wherein the X-ray image data is obtained using a multi-viewX-ray scanner and wherein said image of the piece of luggage andcontents thereof is a first X-ray image obtained by subjecting the pieceof luggage to X-rays in a first orientation, said X-ray image dataconveying a second X-ray image of the piece of luggage, the second imagebeing obtained by subjecting the piece of luggage item to X-rays in asecond orientation.
 18. A method as defined in claim 17, comprisingprocessing the first X-ray image and the second X-ray image to derivethe detection results.
 19. A non-transitory computer readable storagemedium storing a program element suitable for execution by a computingapparatus for use in performing security screening on luggage, saidcomputing apparatus comprising: a) a memory unit; b) a processoroperatively connected to said memory unit, said program element whenexecuting on said processor being configured for: i. receiving X-rayimage data conveying an image of a piece of luggage and contentsthereof, the X-ray image data being generated by scanning the piece ofluggage with an X-ray scanner; ii. processing the X-ray image data todetect if a liquid product is present in the piece of luggage, theliquid product being comprised of a container holding a body of liquid,wherein processing the X-ray image data includes detecting a liquidproduct signature in the X-ray image data; iii. releasing detectionresults obtained by processing the X-ray image data.
 20. Anon-transitory computer readable storage medium as defined in claim 19,wherein processing the X-ray image data includes identifying in theimage conveyed by the X-ray image data an area of interest.
 21. Anon-transitory computer readable storage medium as defined in claim 20,wherein said program element when executing on said processor isconfigured for: a) processing the X-ray image data to estimate athickness of the identified area of interest; b) selectivelydisqualifying the identified area of interest as being unlikely to beassociated with the liquid product at least in part by processing theestimated thickness of the identified area of interest in combinationwith attenuation information derived from the X-ray image data.
 22. Anon-transitory computer readable storage medium as defined in claim 20,wherein said program element when executing on said processor isconfigured for: i. processing the X-ray image data to derive geometricinformation associated with the identified area of interest; ii.selectively disqualifying the identified area of interest for beingunlikely to be associated with the liquid product at least in part basedon the derived geometric information.
 23. A non-transitory computerreadable storage medium as defined in claim 20, wherein said programelement when executing on said processor is configured for: i.processing the identified area of interest to derive density informationassociated with the identified area of interest; ii. selectivelydisqualifying the identified area of interest for being unlikely to beassociated with the liquid product at least in part based on the deriveddensity information.
 24. A non-transitory computer readable storagemedium as defined in claim 20, wherein said program element whenexecuting on said processor is configured for: (1) processing theidentified area of interest to determine whether it is likely to beassociated with at least one object in a set of imposter objects; (2)selectively disqualifying the identified area of interest for beingunlikely to be associated with the liquid product at least in part basedon results obtained in (1).
 25. A non-transitory computer readablestorage medium as defined in claim 24, wherein the set of imposterobjects includes at least one object selected from the set consisting ofCDs, Shoes, books and binders.
 26. A non-transitory computer readablestorage medium as defined in claim 20, wherein identifying the area ofinterest includes: a) deriving compensated X-ray image data at least inpart by compensating the X-ray image data for an amount of objectinduced X-ray attenuation resulting from X-rays travelling through anobject in the piece of luggage, the object being unlikely to be part ofthe liquid product; b) using the compensated X-ray image data whenidentifying the area of interest in the image.
 27. A non-transitorycomputer readable storage medium as defined in claim 19, wherein saidprogram element when executing on said processor is configured forrendering a visual representation of the piece of luggage, the visualrepresentation of the piece of luggage being derived at least in partbased on the X-ray image data and the detection results and conveying toan operator a location in the piece of luggage corresponding to theliquid product.
 28. A non-transitory computer readable storage medium asdefined in claim 27, wherein the location in the piece of luggagecorresponding to the liquid product is conveyed by highlighting aportion of the visual representation of the piece of luggage.
 29. Anapparatus for use in performing security screening on luggage, saidapparatus comprising: a) an input for receiving X-ray image dataconveying an image of a piece of luggage and contents thereof, the X-rayimage data being generated by scanning the piece of luggage with anX-ray scanner; b) a processing unit programmed for processing the X-rayimage data received at the input to detect if a liquid product ispresent in the piece of luggage, the liquid product being comprised of acontainer holding a body of liquid, wherein processing the X-ray imagedata includes detecting a liquid product signature in the X-ray imagedata; c) an output for releasing detection results obtained by theprocessing unit.
 30. An apparatus as defined in claim 29, whereinprocessing the X-ray image data includes identifying in the imageconveyed by the X-ray image data an area of interest.
 31. An apparatusas defined in claim 30, wherein said processing unit is furtherprogrammed for: a) processing the X-ray image data to estimate athickness of the identified area of interest; b) selectivelydisqualifying the identified area of interest as being unlikely to beassociated with the liquid product at least in part by processing theestimated thickness of the identified area of interest in combinationwith attenuation information derived from the X-ray image data.
 32. Anapparatus as defined in claim 30, wherein said processing unit isfurther programmed for: i. processing the X-ray image data to derivegeometric information associated with the identified area of interest;ii. selectively disqualifying the identified area of interest for beingunlikely to be associated with the liquid product at least in part basedon the derived geometric information.
 33. An apparatus as defined inclaim 30, wherein said processing unit is further programmed for: i.processing the identified area of interest to derive density informationassociated with the identified area of interest; ii. selectivelydisqualifying the identified area of interest for being unlikely to beassociated with the liquid product at least in part based on the deriveddensity information.
 34. An apparatus as defined in claim 30, whereinsaid processing unit is further programmed for: (1) processing theidentified area of interest to determine whether it is likely to beassociated with at least one object in a set of imposter objects; (2)selectively disqualifying the identified area of interest for beingunlikely to be associated with the liquid product at least in part basedon results obtained in (1).
 35. An apparatus as defined in claim 34,wherein the set of imposter objects includes at least one objectselected from the set consisting of CDs, Shoes, books and binders. 36.An apparatus as defined in claim 30, wherein identifying the area ofinterest includes: a) deriving compensated X-ray image data at least inpart by compensating the X-ray image data for an amount of objectinduced X-ray attenuation resulting from X-rays travelling through anobject in the piece of luggage, the object being unlikely to be part ofthe liquid product; b) using the compensated X-ray image data whenidentifying the area of interest in the image.
 37. An apparatus asdefined in claim 29, wherein said processing unit is further programmedfor rendering a visual representation of the piece of luggage, thevisual representation of the piece of luggage being derived at least inpart based on the X-ray image data and the detection results andconveying to an operator a location in the piece of luggagecorresponding to the liquid product.
 38. An apparatus as defined inclaim 37, wherein the location in the piece of luggage corresponding tothe liquid product is conveyed by highlighting a portion of the visualrepresentation of the piece of luggage.
 39. A system suitable forperforming security screening on luggage, said system comprising: a) aninspection device for performing an X-ray inspection on a piece ofluggage using penetrating radiation to generate X-ray image dataassociated with the piece of luggage under inspection; b) an apparatuscomprising: i. an input for receiving X-ray image data conveying animage of the piece of luggage and contents thereof, the X-ray image databeing generated by scanning the piece of luggage with an X-ray scanner;ii. a processing unit programmed for processing the X-ray image datareceived at the input to detect if a liquid product is present in thepiece of luggage, the liquid product being comprised of a containerholding a body of liquid, wherein processing the X-ray image dataincludes detecting a liquid product signature in the X-ray image data;iii. an output for releasing detection results obtained by theprocessing unit; c) a display screen in communication with the output ofsaid apparatus for visually conveying to an operator information derivedat least in part based on the detection results released by theapparatus.
 40. A system as defined in claim 39, wherein the inspectiondevice is a multi-view X-ray machine.